Compromised beta-cell identity in type 2 diabetes
Bibliographic record
Abstract
Abstract Compromised beta-cell identity is emerging as an important contributor of beta-cell dysfunction in type 2 diabetes (T2D). Several studies suggest that hyperglycemia induces the inactivation of transcription factors involved in mature beta-cell identity. More specifically, chronic hyperglycemia leads to the downregulation of PDX1 and MAFA, two important beta-cell identity transcription factors and regulators of insulin promoter activity. Regulation of these transcription factors depends on interactions between multiple signaling cascades and processes driven by complex non-linear dynamics and taking place in different cellular compartments. To better understand these non-linear dynamics, we developed an integrated mathematical model of the underlying mechanisms regulating these key transcription factors. Our model was able to reproduce experimentally measured variations in the levels of PDX1, MAFA and insulin mRNA under different glucose concentrations. We used this model to simulate scenarios that could allow to restore PDX1 and MAFA levels and therefore insulin gene expression. From these simulations, FOXO1 emerged as an important target for the restoration of beta-cell identity. Author summary Glucose regulation depends on the secretion of insulin by beta-cells and uptake of glucose by the peripheral cells mediated by the action of insulin. In type 2 diabetes both aspects can be compromised. Defective insulin secretion results from compromised beta-cell function. One of the reasons behind compromised beta-cell function is that beta-cells fail to express one or more of the genes involved in insulin production and secretion and thus maintenance of beta-cell identity. The processes involved in the regulation of insulin production and secretion are complex. In this work, we are particularly interested in the role and downregulation of transcription factors, PDX1 and MAFA, which are critical regulators of insulin production, in relation with compromised beta-cell identity and function in the presence of chronic hyperglycemia. To understand better these complex processes, we use mathematical modelling which enables to generate hypotheses and simulate scenarios to extend our understanding of the mechanisms leading to compromised beta-cell function in the presence of chronic hyperglycemia. Our model and similar models can serve to identify therapeutical targets in beta-cells in order to restore their function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".